milking cows for data — not just dairy products

In the mid-
In their 1970 s, the average dairy farm in the United States had 25 cows.
Today, there are more than 3,000 businesses-
This number was almost unheard of 25 years ago.
Without the latest advances in computing and automation, it would be difficult, or even impossible, to efficiently manage large groups.
Most dairy farms now have dairy farms and free dairy farms.
Booth housing, twice or three times per personhour.
In order to reduce the health problem of cows and improve the quality of milk, the milking unit is automatically separated, while the cow ID forwarder allows farmers to automatically record production data.
Recent major technological advances affecting the United StatesS.
Dairy industry is the development direction of automatic milking system.
Or \"robot\" milkers.
At the Kellogg dairy center at the University of Connecticut, we are using robotic milking machines and other sensors to monitor 100 cows and their physical environment.
Through this work launched this spring, we hope to be able to monitor the behavior and health of individual dairy cows in real time to improve productivity and animal healthbeing.
Big data and milk robots can harvest milk without human participation.
In fact, cows enter the machine without direct supervision and decide when to milk.
The robot system automatically recognizes cows and applies a disinfectant nipple spray before the robot arm connects the nipple Cup to milk.
This is very different from milking in the living room, where the manager usually decides when to milk three times a day.
Each robot milking unit serves 50 to 55 cows.
Given the high price of the earlier version of robot milkers, the size of the United States is also very largeS.
Before 2010, American dairy farms had little interest in robotic dairy farms.
However, the number of automatic milking systems in the country increased to more than 2,500 in 2013, mainly due to improvements in the design of the new model.
At present, more than 35,000 automatic milking systems are running around the world.
These newer machines can not only collect milk effectively, but also collect more information about production, milk composition and cow behavior.
This allows producers to make more informed management decisions.
Through a robotic milking system, cows can perform.
They decide when to eat, meditate, rest or milk.
They also need to spend less than an hour a day on actual milking;
Before a robot can milk, it usually takes three to five hours a day.
We want to know: what are they doing for the rest of the day? How does this behavior affect production, or does it help to indicate health status on its own, and milking units are unable to collect this information, which is very useful for early detection of whether a particular cow is developing health issues. Our “cow-CPS” —a cyber-
Physical systems including cows, robotic milking, cameras and other sensors-
Will keep track of the data of our cows at any time.
This will tell us where the cows will go when there is no milking, among other things;
When they decide to eat, rest, or do other activities;
And the ingredients of their milk.
Sensors placed inside the body will even tell us about the pH value inside their stomach, which may be a key indicator of any digestive problem.
Optimizing dairiesWe hopes that all of this data will enable us to make timely decisions at the individual level of the cow, which is not easy to do in a large group of cows.
This \"precision dairy\" can help us understand the activities of a cow --
Eat, stand, rest, milk
Affects her milk production, milk quality and health.
We plan to analyze data with the help of machine learning, an artificial intelligence that can find patterns in a large amount of information.
The computer compares the data with a model of how dairy products should operate under ideal conditions.
Our model captures key performance features
Quality and production efficiency of milk
As well as related constraints such as personal health and reproductive status.
With the operation of the dairy farm, the real
Time data will allow us to assess how far our real farm is from the ideal one.
We can then combine this information with mathematical optimization algorithms to determine how we should modify or adjust the process.
For example, the algorithm may suggest adjusting the type of nipple drip, the nutritional content of the feed, or the amount of time each cow spends on feeding.
We hope that our work will enable dairy farmers across the United States. S.
Better management of individual cows in a group environment-not only to increase milk production, but also to enhance cow health.
Matthew Stuber, assistant professor of chemical and biological molecular engineering, University of Connecticut;
Gary Kazmer, associate professor of breastfeeding at the University of Connecticut;